Models utilizing case-based reasoning methods [1]} first retrieve similar cases which are then used in synthesising the current answer.
For question answering, such an architecture has been proposed by [2]}.
The retrieved similar queries also contain their logical forms (e.g. SQL or a graph query), based on which a logical form for the current query is constructed using a Transformer-based model BigBird [3]}.
This logical form is then executed against a symbolic knowledge base and further refined by another component.
The refinement step solves the issue of sparse knowledge bases and aligns each logical query edge
This is performed either by pre-trained KB embeddings [4]} and similarity search or by using surface form similarity of edge names.
\(& \texttt {Encoder:} & k = \textsc {Roberta}_\textsc {Base}(q)\\& \texttt {Symbolic knowledge base:} & \mathcal {B_S} = \lbrace  (\textsc {Roberta}_\textsc {Base}(d), f) | (d,f) \in \mathcal {KB}_S \rbrace  \\& \texttt {Retriever:} & C = \arg \max ^n_{d\in B}\, \langle q, d \rangle \\& \texttt {Aggregator:} & \hspace{-56.9055pt} \xi = \textsc {BigBird}(q \texttt {[SEP]} C_1^q \texttt {[SEP]} C_1^f \texttt {[SEP]} \ldots \texttt {[SEP]} C_n^q \texttt {[SEP]} C_n^f) \\& & \xi ^{\prime } = \textsc {Align}(\xi , \mathcal {KB}) \\& \texttt {Retriever:} & o = \texttt {Execute}(\xi ^{\prime }, \mathcal {KB})\) 
